We use the Mask R-CNN detection method [1]} with ViT backbone as our detection method. In order to obtain features compatible with the Feature Pyramid Network (FPN) design [2]}, we use max pooling and transposed convolution operations similar to [3]}. To accommodate for the variable resolution we replace the absolute positional encoding for our models and the baselines with sinusoidal positional encoding [4]}. All models are trained using the 3x schedule (36 epochs) unless mentioned otherwise. We use the training hyper-parameters used by [5]}.
